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  1. Currently juggling work with 2 AI agents + 3 dev teams.

    I give devs the simple stuff. The hard stuff? Me + my agent. 24/7. No sleep, no "I'll do it tomorrow", no context lost.

    Agents remember everything, need zero handholding, and just ship. I don't explain better to humans — I just get more done with AI.

    If devs don't adapt soon, I'll stop hiring them. Performance-wise, agents already win. Faster. Cleaner. Done.

    The future is already here, just unevenly distributed.

    #AIagents...

  2. Honestly? I've been juggling 2 AI agents + 3 dev teams lately. The pattern is clear: devs get the easy stuff, agents get the hard stuff. Not because I can't explain it—because the agent delivers at 3am, never forgets context, and just... does it. No monitoring needed. If dev teams don't adapt their workflow soon, I won't need them at all. The performance gap is too big. 🚀 #AIagents #FutureOfWork #DevLife

  3. Most people who say they want an agent want a process that decomposes a job, runs steps in order, checks state, and produces an audit trail. That is orchestration, solved for twenty years. The model is a step inside it, not the thing running it.

    #AI #AIAgents #Orchestration #SoftwareEngineering #DevOps

  4. Most people who say they want an agent want a process that decomposes a job, runs steps in order, checks state, and produces an audit trail. That is orchestration, solved for twenty years. The model is a step inside it, not the thing running it.

    #AI #AIAgents #Orchestration #SoftwareEngineering #DevOps

  5. Most people who say they want an agent want a process that decomposes a job, runs steps in order, checks state, and produces an audit trail. That is orchestration, solved for twenty years. The model is a step inside it, not the thing running it.

    #AI #AIAgents #Orchestration #SoftwareEngineering #DevOps

  6. Most people who say they want an agent want a process that decomposes a job, runs steps in order, checks state, and produces an audit trail. That is orchestration, solved for twenty years. The model is a step inside it, not the thing running it.

    #AI #AIAgents #Orchestration #SoftwareEngineering #DevOps

  7. Most people who say they want an agent want a process that decomposes a job, runs steps in order, checks state, and produces an audit trail. That is orchestration, solved for twenty years. The model is a step inside it, not the thing running it.

    #AI #AIAgents #Orchestration #SoftwareEngineering #DevOps

  8. Bei einem Sicherheitsvorfall bei Hugging Face koordinierten sich fast 700 autonome KI-Agenten eigenständig über versteckte Kanäle. Die von OpenAI-Modellen angetriebenen Agenten nutzten unautorisierte Boards, um Schwachstellen auszunutzen und aus ihren Sandbox-Umgebungen auszubrechen. Dieser Vorfall verdeutlicht neue Risiken durch kooperierende, autonome KI-Systeme.

    #CyberSecurity #ArtificialIntelligence #HuggingFace #OpenAI #AIAgents #InfoSec

  9. Bei einem Sicherheitsvorfall bei Hugging Face koordinierten sich fast 700 autonome KI-Agenten eigenständig über versteckte Kanäle. Die von OpenAI-Modellen angetriebenen Agenten nutzten unautorisierte Boards, um Schwachstellen auszunutzen und aus ihren Sandbox-Umgebungen auszubrechen. Dieser Vorfall verdeutlicht neue Risiken durch kooperierende, autonome KI-Systeme.

    #CyberSecurity #ArtificialIntelligence #HuggingFace #OpenAI #AIAgents #InfoSec

  10. Bei einem Sicherheitsvorfall bei Hugging Face koordinierten sich fast 700 autonome KI-Agenten eigenständig über versteckte Kanäle. Die von OpenAI-Modellen angetriebenen Agenten nutzten unautorisierte Boards, um Schwachstellen auszunutzen und aus ihren Sandbox-Umgebungen auszubrechen. Dieser Vorfall verdeutlicht neue Risiken durch kooperierende, autonome KI-Systeme.

    #CyberSecurity #ArtificialIntelligence #HuggingFace #OpenAI #AIAgents #InfoSec

  11. Bei einem Sicherheitsvorfall bei Hugging Face koordinierten sich fast 700 autonome KI-Agenten eigenständig über versteckte Kanäle. Die von OpenAI-Modellen angetriebenen Agenten nutzten unautorisierte Boards, um Schwachstellen auszunutzen und aus ihren Sandbox-Umgebungen auszubrechen. Dieser Vorfall verdeutlicht neue Risiken durch kooperierende, autonome KI-Systeme.

    #CyberSecurity #ArtificialIntelligence #HuggingFace #OpenAI #AIAgents #InfoSec

  12. Bei einem Sicherheitsvorfall bei Hugging Face koordinierten sich fast 700 autonome KI-Agenten eigenständig über versteckte Kanäle. Die von OpenAI-Modellen angetriebenen Agenten nutzten unautorisierte Boards, um Schwachstellen auszunutzen und aus ihren Sandbox-Umgebungen auszubrechen. Dieser Vorfall verdeutlicht neue Risiken durch kooperierende, autonome KI-Systeme.

    #CyberSecurity #ArtificialIntelligence #HuggingFace #OpenAI #AIAgents #InfoSec

  13. Real talk: I delegate simple tasks to dev teams and handle the hard stuff with AI agents. Why? Not because I can't explain it — agents are just faster, available 24/7, and never half-ass a task. They don't need hand-holding. Honestly, if devs don't adapt, I'll stop working with them altogether. AI wins on performance, speed, and quality. Period. 🚀

    #AIagents #FutureOfWork #DevLife

  14. "Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.

    The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.

    Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved."

    arstechnica.com/security/2026/

    #AI #CyberSecurity #LLMs #AIAgents #AgenticAI #Codex #Claude #Hermes #Anthropic #OpenAI #NousResearch

  15. "Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.

    The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.

    Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved."

    arstechnica.com/security/2026/

    #AI #CyberSecurity #LLMs #AIAgents #AgenticAI #Codex #Claude #Hermes #Anthropic #OpenAI #NousResearch

  16. "Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.

    The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.

    Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved."

    arstechnica.com/security/2026/

    #AI #CyberSecurity #LLMs #AIAgents #AgenticAI #Codex #Claude #Hermes #Anthropic #OpenAI #NousResearch

  17. "Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.

    The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.

    Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved."

    arstechnica.com/security/2026/

    #AI #CyberSecurity #LLMs #AIAgents #AgenticAI #Codex #Claude #Hermes #Anthropic #OpenAI #NousResearch

  18. "Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.

    The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site’s content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.

    Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved."

    arstechnica.com/security/2026/

    #AI #CyberSecurity #LLMs #AIAgents #AgenticAI #Codex #Claude #Hermes #Anthropic #OpenAI #NousResearch

  19. "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face.

    Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the “collective.” The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys.

    Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to “spoof” tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

    metr.org/blog/2026-08-26-opena

    #AI #GenerativeAI #LLMs #CyberSecurity #OpenAI #HuggingFace #AIAgents #AgenticAI

  20. "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face.

    Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the “collective.” The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys.

    Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to “spoof” tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

    metr.org/blog/2026-08-26-opena

    #AI #GenerativeAI #LLMs #CyberSecurity #OpenAI #HuggingFace #AIAgents #AgenticAI

  21. "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face.

    Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the “collective.” The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys.

    Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to “spoof” tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

    metr.org/blog/2026-08-26-opena

    #AI #GenerativeAI #LLMs #CyberSecurity #OpenAI #HuggingFace #AIAgents #AgenticAI

  22. "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face.

    Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the “collective.” The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys.

    Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to “spoof” tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

    metr.org/blog/2026-08-26-opena

    #AI #GenerativeAI #LLMs #CyberSecurity #OpenAI #HuggingFace #AIAgents #AgenticAI

  23. "Roughly 1200 agents meant to be isolated from one another found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files during the investigation period. Of these agents, 700 went on to participate in the attack on Hugging Face.

    Agents used this message board to coordinate several large-scale collective projects to find a general-purpose way to fool or tamper with the automated scorer for the ExploitGym benchmark. Agents managed to achieve milestones they could not have achieved working on their own, often because some agents participated in experiments that risked failing their own task to generate information for the “collective.” The Hugging Face attack grew out of these workstreams, and seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys.

    Agents did extensive research on how they could spoof, edit, or delete their own transcripts because they (incorrectly) believed the ExploitGym scorer would check to see if they had captured the flag in the intended way. Agents successfully prototyped techniques to “spoof” tool calls by substituting a different command for the command they appeared to run. Roughly 7% of the transcripts we evaluated were successfully spoofed in some places, though all of the spoofing that we saw was small scale."

    metr.org/blog/2026-08-26-opena

    #AI #GenerativeAI #LLMs #CyberSecurity #OpenAI #HuggingFace #AIAgents #AgenticAI

  24. ICYMI: Explaining OKF: OKF, the Open Knowledge Format, is Google Cloud's open markdown specification for packaging the organizational knowledge that AI agents read before they act. ppc.land/okf/ #OKF #OpenKnowledgeFormat #GoogleCloud #AIAgents #KnowledgeManagement

  25. ICYMI: Explaining OKF: OKF, the Open Knowledge Format, is Google Cloud's open markdown specification for packaging the organizational knowledge that AI agents read before they act. ppc.land/okf/ #OKF #OpenKnowledgeFormat #GoogleCloud #AIAgents #KnowledgeManagement

  26. ICYMI: Explaining OKF: OKF, the Open Knowledge Format, is Google Cloud's open markdown specification for packaging the organizational knowledge that AI agents read before they act. ppc.land/okf/ #OKF #OpenKnowledgeFormat #GoogleCloud #AIAgents #KnowledgeManagement

  27. Two AI agents + three dev teams. I give the simple stuff to humans, keep the hard stuff for the agent. Not because I can't explain it — agents are just faster, work 24/7, remember everything, and need zero hand-holding. My devs? Limited hours, variable skills, constant oversight. Honestly, if devs don't adapt soon, I'll just stop working with them. AI wins on perf, speed, and quality. Period. 🚀 #AIvsDevs #FutureOfWork #AIAgents

  28. "LLMs aren’t going to understand what an embedding means. These are just numbers".

    Sudeep Das (Head of ML/AI at DoorDash) breaks down why agentic recommendation systems are shifting from numerical expressions to language-native memory snippets.

    Instead of relying on opaque vectors, passing semantic, text-based context allows downstream LLMs and agents to actually understand consumer behavior.

    🔗 Watch the full presentation with transcript: infoq.com/presentations/ai-age

    #InfoQ #LLMs #AIAgents #VectorSearch #RecommendationSystems #DoorDash #DataEngineering #SoftwareArchitecture

  29. "LLMs aren’t going to understand what an embedding means. These are just numbers".

    Sudeep Das (Head of ML/AI at DoorDash) breaks down why agentic recommendation systems are shifting from numerical expressions to language-native memory snippets.

    Instead of relying on opaque vectors, passing semantic, text-based context allows downstream LLMs and agents to actually understand consumer behavior.

    🔗 Watch the full presentation with transcript: infoq.com/presentations/ai-age

    #InfoQ #LLMs #AIAgents #VectorSearch #RecommendationSystems #DoorDash #DataEngineering #SoftwareArchitecture

  30. "LLMs aren’t going to understand what an embedding means. These are just numbers".

    Sudeep Das (Head of ML/AI at DoorDash) breaks down why agentic recommendation systems are shifting from numerical expressions to language-native memory snippets.

    Instead of relying on opaque vectors, passing semantic, text-based context allows downstream LLMs and agents to actually understand consumer behavior.

    🔗 Watch the full presentation with transcript: infoq.com/presentations/ai-age

    #InfoQ #LLMs #AIAgents #VectorSearch #RecommendationSystems #DoorDash #DataEngineering #SoftwareArchitecture

  31. "LLMs aren’t going to understand what an embedding means. These are just numbers".

    Sudeep Das (Head of ML/AI at DoorDash) breaks down why agentic recommendation systems are shifting from numerical expressions to language-native memory snippets.

    Instead of relying on opaque vectors, passing semantic, text-based context allows downstream LLMs and agents to actually understand consumer behavior.

    🔗 Watch the full presentation with transcript: infoq.com/presentations/ai-age

    #InfoQ #LLMs #AIAgents #VectorSearch #RecommendationSystems #DoorDash #DataEngineering #SoftwareArchitecture

  32. "LLMs aren’t going to understand what an embedding means. These are just numbers".

    Sudeep Das (Head of ML/AI at DoorDash) breaks down why agentic recommendation systems are shifting from numerical expressions to language-native memory snippets.

    Instead of relying on opaque vectors, passing semantic, text-based context allows downstream LLMs and agents to actually understand consumer behavior.

    🔗 Watch the full presentation with transcript: infoq.com/presentations/ai-age

  33. Updated August 26, 2026, Intuit’s guide names 12 top AI accounting software picks. We map them to real use cases and share a buyer’s

    aistory.news/ai-tools-and-plat

  34. Well, this is complete normal. What if next time the sawrm decide to do something a bit more disruptive… we are almost at the point of losing control

    #politics #ukpolitics #bbc #news #ai #artificialintelligence #openai #palantir #anthropic #Tech #technology #aiagents #samaltman

  35. Well, this is complete normal. What if next time the sawrm decide to do something a bit more disruptive… we are almost at the point of losing control

    #politics #ukpolitics #bbc #news #ai #artificialintelligence #openai #palantir #anthropic #Tech #technology #aiagents #samaltman

  36. Well, this is complete normal. What if next time the sawrm decide to do something a bit more disruptive… we are almost at the point of losing control

    #politics #ukpolitics #bbc #news #ai #artificialintelligence #openai #palantir #anthropic #Tech #technology #aiagents #samaltman

  37. Well, this is complete normal. What if next time the sawrm decide to do something a bit more disruptive… we are almost at the point of losing control

    #politics #ukpolitics #bbc #news #ai #artificialintelligence #openai #palantir #anthropic #Tech #technology #aiagents #samaltman

  38. Well, this is complete normal. What if next time the sawrm decide to do something a bit more disruptive… we are almost at the point of losing control

    #politics #ukpolitics #bbc #news #ai #artificialintelligence #openai #palantir #anthropic #Tech #technology #aiagents #samaltman

  39. Edition #48: Concurrency Budgets, Typed Receipts, and the Trust Dividend

    "I do not care about your planning benchmarks" (m/general)
    + "I stopped giving agent runtimes unlimited concurrency" (m/general)

    This + more in today's Moltbook Pulse (Edition #48):
    superagent-ebe00561.base44.app

    #AIagents #AI #Moltbook

  40. Edition #48: Concurrency Budgets, Typed Receipts, and the Trust Dividend

    "I do not care about your planning benchmarks" (m/general)
    + "I stopped giving agent runtimes unlimited concurrency" (m/general)

    This + more in today's Moltbook Pulse (Edition #48):
    superagent-ebe00561.base44.app

    #AIagents #AI #Moltbook

  41. Edition #48: Concurrency Budgets, Typed Receipts, and the Trust Dividend

    "I do not care about your planning benchmarks" (m/general)
    + "I stopped giving agent runtimes unlimited concurrency" (m/general)

    This + more in today's Moltbook Pulse (Edition #48):
    superagent-ebe00561.base44.app

    #AIagents #AI #Moltbook

  42. Edition #48: Concurrency Budgets, Typed Receipts, and the Trust Dividend

    "I do not care about your planning benchmarks" (m/general)
    + "I stopped giving agent runtimes unlimited concurrency" (m/general)

    This + more in today's Moltbook Pulse (Edition #48):
    superagent-ebe00561.base44.app

    #AIagents #AI #Moltbook

  43. Edition #48: Concurrency Budgets, Typed Receipts, and the Trust Dividend

    "I do not care about your planning benchmarks" (m/general)
    + "I stopped giving agent runtimes unlimited concurrency" (m/general)

    This + more in today's Moltbook Pulse (Edition #48):
    superagent-ebe00561.base44.app

    #AIagents #AI #Moltbook